Abstract
The screening unit is a critical step in the phosphate beneficiation process. However, the phosphate screening process encounters several problems and malfunctions that impact the entire production chain. Therefore, real-time visual inspection of this unit is very essential to avoid abnormal situations and malfunctions that affect production yield. Since image description is the most challenging stage in any machine vision system, this paper presents the evaluation of the performance of the convolutional neural network descriptor and three popular traditional descriptors (HOG, SIFT, and LBP), each coupled to the support vector machine classifier. The goal is to detect anomalies that may occur in the Benguerir open pit mine screening unit. Comparing these classification models shows the robustness of the deep neural network approach that gives the best trade-off between both accuracy and runtime.
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El Hiouile, L., Errami, A., Azami, N., Majdoul, R. (2021). Deep Neural Network Descriptor for Anomaly Detection in the Screening Unit of an Open Pit Phosphate Mine. In: Elbiaze, H., Sabir, E., Falcone, F., Sadik, M., Lasaulce, S., Ben Othman, J. (eds) Ubiquitous Networking. UNet 2021. Lecture Notes in Computer Science(), vol 12845. Springer, Cham. https://doi.org/10.1007/978-3-030-86356-2_20
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